Do US States’ Governors’ Attitude Affect Covid 19’s Development?

knitr::opts_chunk$set(echo=TRUE,eval=TRUE, message=FALSE, warning=FALSE)

library(httr)
set_config(use_proxy(url="127.0.0.1",port=15236))

library(tidyverse)
library(lubridate)
library(rtweet)
library(leaflet)
library(tigris)
library(plotly)
library(readxl)
library(ggthemes)
library(leaflet)
library(wordcloud)

governor_tweets <- readRDS("governor_tweets.RDS")
governor <- read_csv("governors_twitter1.csv")%>%
 select(state, governor, party, twitter_handle, state_abbr)

Filtering governor tweets about COVID19 and Election

d <- filter(governor_tweets, grepl("COVID19|corona|virus|SocialDistancing|distancing|mask|ballot|elect|vote", text))
tweets <- d %>%
        filter(is_retweet==FALSE) %>%
        select(text,screen_name,hashtags,retweet_count)%>%
        left_join(governor, by=c("screen_name"="twitter_handle"))

#### word cloud and sentiment
library(tm)
corpus0 <-VCorpus(VectorSource(tweets$text))
# remove fully capitalized words 
corpus1 = tm_map(corpus0, content_transformer(function(x) gsub("\\b[A-Z]+\\b","",x))) 
corpus1 = tm_map(corpus0, content_transformer(function(x) gsub("[^[:alnum:][:space:]]","",x))) 
# create a function to space and remove URLs
removeURL <- content_transformer(function(x) gsub("(f|ht)tp(s?)://\\S+", "", x, perl=T))
corpus1 = tm_map(corpus1, removeURL)
# clean text
clean_corpus <- function(corpus){
  corpus <- tm_map(corpus, removePunctuation)
  # corpus <- tm_map(corpus, content_transformer(removeNumPunct)) # from qdap
  corpus <- tm_map(corpus, content_transformer(tolower))
  #corpus <- tm_map(corpus, content_transformer(replace_symbol)) # from qdap
  corpus <- tm_map(corpus, removeWords, c(stopwords("en")))
    # We could add more stop words as above
  corpus <- tm_map(corpus, removeNumbers)
  corpus <- tm_map(corpus, stripWhitespace)
  return(corpus)
}

clean_corpus<-clean_corpus(corpus1)
  

library("SnowballC")
corpus1_dtm <- DocumentTermMatrix(clean_corpus)
corpus1_dtm <- as.matrix(corpus1_dtm)

adt = as.data.frame(corpus1_dtm)
all = colSums(adt)
all = data.frame('all'=all)
all$labels = rownames(all)
all = all[order(all[,1],decreasing = TRUE),]
top20all = all[1:20,]
top20all$labels = factor(top20all$labels, levels = top20all$labels)

g_top20 <- ggplot(top20all,aes(x = labels, y = all)) + 
  geom_bar(stat = 'identity',fill="coral")+ylim(c(0,4000))+
  coord_flip() +
  labs(title="Top 20 Words in US Governors' Covid 19 and Election Related Tweets",
    x='Words',y = 'Frequency',caption= "Source:Twitter"
    )+theme_pander()+
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), 
        axis.text.x = element_text(size=12, hjust=0.1,vjust=1), 
        axis.text.y = element_text(size=10, face="bold"), 
        axis.title.y = element_text(size=10,face="bold"),
        axis.title.x = element_blank(),
        plot.caption = element_text(size=11, face="bold"),
        panel.grid.major.x = element_blank())
g_top20 

positive & negative word clouds by party

pos <- read.table("positive-words.txt", as.is=T)
neg <- read.table("negative-words.txt", as.is=T)
vector = c()
sentiment <- function(words){
  require(quanteda)
  tok <- quanteda::tokens(words)
  pos.count <- sum(tok[[1]]%in%pos[,1])
  #cat("\n positive words:",tok[[1]][which(tok[[1]]%in%pos[,1])],"\n")
  neg.count <- sum(tok[[1]]%in%neg[,1])
  #cat("\n negative words:",tok[[1]][which(tok[[1]]%in%neg[,1])],"\n")
  out <- (pos.count - neg.count)/(pos.count+neg.count)
  #cat("\n Tone of Document:",out)
  out
}
for (i in 1:7267){
  vector[i] <- sentiment(tweets$text[i])
}
tweets["Tone_of_Text"] = vector

#plot positive vs negative words by party
#split tweets by party
d_tweets <- tweets%>%
    filter(grepl( "D", party))

r_tweets <- tweets%>%
    filter(grepl( "R", party))

Plot Democratic Positive & Negative Words

# filter positive texts and negative texts
d_pos_texts <- d_tweets %>%
  filter(Tone_of_Text > 0) %>%
  select(text)
d_neg_texts <- d_tweets %>%
  filter(Tone_of_Text < 0) %>%
  select(text)
com_texts <- c(d_pos_texts,d_neg_texts)
com_corpus <-VCorpus(VectorSource(com_texts))
com_corpus = tm_map(com_corpus, content_transformer(function(x) gsub("\\b[A-Z]+\\b","",x))) 
com_corpus = tm_map(com_corpus, content_transformer(function(x) gsub("[^[:alnum:][:space:]]","",x))) 
com_corpus = tm_map(com_corpus, content_transformer(tolower))  
com_corpus = tm_map(com_corpus, removeNumbers)
com_corpus = tm_map(com_corpus, removePunctuation)
com_corpus = tm_map(com_corpus, removeWords, c(stopwords("en")))
com_corpus = tm_map(com_corpus, stripWhitespace)
#create document-term-matrix 
com_dtm <- DocumentTermMatrix(com_corpus)
com_dtm_ma <- as.matrix(com_dtm)
com_tdm <- TermDocumentMatrix(com_corpus)
com_ma <- as.matrix(com_tdm)
colnames(com_ma) <- c("positive words","negative words")
comparison.cloud(com_ma, colors = c("steelblue","lightsteelblue2"), title.size= 3,
                 scale = c(6,2), max.words = 100, shape = "circle")

Plot Republican Positive & Negative Words

# filter positive texts and negative texts
r_pos_texts <- r_tweets %>%
  filter(Tone_of_Text > 0) %>%
  select(text)
r_neg_texts <- r_tweets %>%
  filter(Tone_of_Text < 0) %>%
  select(text)
com_texts <- c(r_pos_texts,r_neg_texts)
com_corpus <-VCorpus(VectorSource(com_texts))
com_corpus = tm_map(com_corpus, content_transformer(function(x) gsub("\\b[A-Z]+\\b","",x)))
com_corpus = tm_map(com_corpus, content_transformer(function(x) gsub("[^[:alnum:][:space:]]","",x))) 
com_corpus = tm_map(com_corpus, content_transformer(tolower))  
com_corpus = tm_map(com_corpus, removeNumbers)
com_corpus = tm_map(com_corpus, removePunctuation)
com_corpus = tm_map(com_corpus, removeWords, c(stopwords("en")))
com_corpus = tm_map(com_corpus, stripWhitespace)
#create document-term-matrix 
com_dtm <- DocumentTermMatrix(com_corpus)
com_dtm_ma <- as.matrix(com_dtm)
com_tdm <- TermDocumentMatrix(com_corpus)
com_ma <- as.matrix(com_tdm)
colnames(com_ma) <- c("positive words","negative words")
comparison.cloud(com_ma, colors = c("coral","lightpink"), title.size= 4,
                 scale = c(6,2), max.words = 100, shape = "circle")

`

Average Sentiment Scores of Governors’ Tweets and COVID Cases

Relationship between Average Sentiment Score of Governors’ tweets about COVID19 and Confirmed COVID Cases by State

#putting average sentiment to leaflet map
content<-paste("State:", allbystate$state, "<br/>",
              "Avg sentiment score of governor tweets about COVID:", allbystate$avg_senti, "<br/>",                "Total number of cases:", allbystate$total_cases, "<br/>",
              "Governor party:", allbystate$party, "<br/>")
pal=colorNumeric(palette="Oranges", allbystate$avg_senti) 
leafletmap2<-leaflet() %>%
  addTiles() %>%
  addProviderTiles("Stamen.TonerLite") %>%
  setView(-98.1156, 38.4204, zoom=4) %>%
  addPolygons(data=allbystate, fillColor=~pal(allbystate$avg_senti), color=allbystate$party, fillOpacity=0.7, weight=2, smoothFactor=0.2, popup=content, label=~stringr::str_c(state, ' See pop-up for more info'), labelOptions=labelOptions(direction='auto'), highlightOptions=highlightOptions(color=allbystate$party, weight=5, bringToFront=TRUE, sendToBack=TRUE)) %>%
  addLegend("bottomright", pal=pal, values=allbystate$avg_senti, title="Average sentiment score of governors' tweets about COVID", opacity=1)
leafletmap2

knitr::include_graphics("1.png")